{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 总资产规模及增长率，看公司实力及成长性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis import ANALYSIS_CONFIGS\n",
    "from analysis.analysis import FinancialAnalysis\n",
    "from analysis.doc_utils import ReportDocument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['all_analysis.json',\n",
       " 'asset_quality_analysis.json',\n",
       " 'asset_indepth_analysis.json',\n",
       " 'asset_fraud_analysis.json',\n",
       " 'profit_analysis.json',\n",
       " 'cash_flow_analysis.json']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANALYSIS_CONFIGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ana = FinancialAnalysis(ANALYSIS_CONFIGS[2])\n",
    "images, titles, fields = ana.images, ana.titles, ana.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总资产增长率</th>\n",
       "      <td>nan%</td>\n",
       "      <td>23.56%</td>\n",
       "      <td>19.29%</td>\n",
       "      <td>12.65%</td>\n",
       "      <td>16.95%</td>\n",
       "      <td>11.63%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  2016           2017           2018            2019  \\\n",
       "资产合计(元)  6,415,202,500  7,926,615,200  9,455,361,500  10,651,922,600   \n",
       "总资产增长率            nan%         23.56%         19.29%          12.65%   \n",
       "\n",
       "                   2020            2021  \n",
       "资产合计(元)  12,457,568,300  13,906,035,200  \n",
       "总资产增长率           16.95%          11.63%  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = ana.init_table('t1')\n",
    "t1['总资产增长率'] = t1['资产合计(元)'].pct_change()\n",
    "\n",
    "ana.format_show_table('t1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f8ccbbfd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 债务压力：资产负债率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>负债合计(元)</th>\n",
       "      <td>2,289,957,600</td>\n",
       "      <td>2,669,143,900</td>\n",
       "      <td>3,324,513,200</td>\n",
       "      <td>3,677,639,200</td>\n",
       "      <td>4,263,789,000</td>\n",
       "      <td>5,139,976,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产负债率</th>\n",
       "      <td>35.70%</td>\n",
       "      <td>33.67%</td>\n",
       "      <td>35.16%</td>\n",
       "      <td>34.53%</td>\n",
       "      <td>34.23%</td>\n",
       "      <td>36.96%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  2016           2017           2018            2019  \\\n",
       "负债合计(元)  2,289,957,600  2,669,143,900  3,324,513,200   3,677,639,200   \n",
       "资产合计(元)  6,415,202,500  7,926,615,200  9,455,361,500  10,651,922,600   \n",
       "资产负债率           35.70%         33.67%         35.16%          34.53%   \n",
       "\n",
       "                   2020            2021  \n",
       "负债合计(元)   4,263,789,000   5,139,976,700  \n",
       "资产合计(元)  12,457,568,300  13,906,035,200  \n",
       "资产负债率            34.23%          36.96%  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = ana.init_table('t2')\n",
    "t2['资产负债率'] = t2['负债合计(元)'] / t2['资产合计(元)']\n",
    "\n",
    "ana.format_show_table('t2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o3NMLkqRp0E8AfCXJv03yeJqpnJsnKLcAeFaSOTTnDardvgi4HdjSZ/uSpGnQzw74TOAq4DrgL6vqe0kOTnLWmHIfBFYA9wF7A59JsgewAbgFWA58te+eS5IGMrfXglW1pP17FfD0Mc/dApwxZts3gWeO81JXtH8XT6WjkqTp5RSMJHWUASBJHWUASFJHGQCS1FEGgCR1lAEgSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkcZAJLUUQaAJHWUASBJHWUASFJHGQCS1FEGgCR1lAEgSR1lAEhSRxkAktRRBoAkddSsBUCS/WarbUlSjwGQZGGSa9vleUm+lOTrSV7fQ90vJVncLn8wyVeSBDhuoJ5LkgYyaQAkWQCsBOa3m94CrK2q3wZekWT3rdQ9Gbi9qta1m4aAG4HDgB8N0nFJ0mB6OQJ4BFgGbG7XlwAXt8urgeHxKiXZG/gIsCnJyKf9AHOBY4Fr+uuyJGk6TBoAVbW5qu4btWk+cHe7/FNg4QRV3w58FvgU8NokLwZuBvYHHgVWJ3nG2EpJlidZk2TNxo0bex+JJGlK+jkJ/DNgt3b5CVt5jcOAT1TVBpojhiVV9THgQuAB4BLghWMrVdWKqhququGhoaE+uidJ6kU/AbAWOLpdPhS4Y4Jy3wcObJeHgfXt8l7A/cCWPtuXJE2DuX3UWQlcluQY4GDg+iTHAwdX1V+MKncOcH6Sd9N84n95koOAm2gC4DLgdYN0XpLUv54DoKqWtH/XJ1lKcxTwJ1X1CHBl+xhd/p+AF4x5mftHLT9m/l+SNHP6OQIY2blfPGlBSdJ2yzl4SeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjjIAJKmjDABJ6igDQJI6ygCQpI4yACSpowwASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjtpmAZBkfpLfSfLkcZ7bPcme26ptSdLkphwASeYm+VGSq9vHs8YpMw+4DDgK+FKSZyZ5SZLvJNkPeAHwi4F7L0nq29w+6hwCfKaq3rmVMgcBH66qLye5FzgaOBR4J/BbwLyqerCPtiVJ06SfKaAjgRcl+WaSC5I8JkSq6jvtzv8w4GXAKuBRYDeaMLhmkE5LkgbXTwDcADy3qp4NzKOZzpnI77Zt3A9cDLwV+AHwkSSvGq9CkuVJ1iRZs3Hjxj66J0nqRT8B8K2q+ud2eQ2waKKCVfV+4G+AN1TVauBVwJ00IfA7E9RZUVXDVTU8NDTUR/ckSb3oJwAuTHJokjnAS4GbxhZIsizJe9rVvYB72+X/AHwNeBioPtqWJE2TfgLg/cCFwDrgG8CNSc4fU+ZzwOIkq4EjgJVJdgEeAH5McyL42333WpI0sClfBVRVN9NcCTTaG8eUeRA4aZzql7R/j59qu5Kk6eU3gSWpowwASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjjIAJKmjDABJ6igDQJI6ygCQpI4yACSpowwASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjjIAJKmjZi0AkixMMm+22pekrus7AJJckOQbSc7otUyS05LckGQ+cGJVPdRv+5KkwfQVAEleDsypqqOAA5Ms6rHMYuB84Ajg5/13W5I0qH6PAJYAF7fLq4CjeywTYB5wAnB5n21LkqZBqmrqlZILgI9X1U1JTgAOr6oPTVYG+CFwCvBF4OXAB6rqqjH1lgPL29WnAd+bcgcHsw/wkxlucybtzONzbDuunXl8szG2/atqaLJCc/t88Z8Bu7XLT2D8I4nHlKmqi5KsBw4ELgVOAv6/AKiqFcCKPvs1sCRrqmp4ttrf1nbm8Tm2HdfOPL7teWz9TgGt5VfTPocCd0yhzCLgdmDLAO1LkgbU7xHA54FrkzwReD7wyiRnVdUZWylzZJI9gA3ALcCngPf333VJ0iD6CoCq2pxkCbAUOKeqNgA3TVLmvvapK9q/i/vq8bY3a9NPM2RnHp9j23HtzOPbbsfW10lgSdKOzzl4SeooA0CSOqpTAZBkzySXJ1mV5HNJdh3vlhbtfYquHaf+l5Jsl+cu+h1bkjOTXN0+bk3yrtkZwcQGGNuBSf4hybok75md3k9ugPEdnuSrSb6e5I9mp/db18vYxivTbp/0djOzacCxjbuPmWmdCgDgZOCjVXUCzdVIr2TM7SqSLABWAvNHV0xyMnB7Va2b6U73qK+xVdV7q2pJVS0Bbgb+eua7Pql+/91OA/6kqhYDJyaZ9Isxs6Tf8Z1L88XKo4GTkjx1hvvdi0nHNk6Z5/Vyu5ntQL9jG3cfMxs6FQBVdV5VjVyFNAS8msferuIRYBmweaRekr2BjwCbkhw3cz3uXb9jG5HkCOCuqrp7Bro7JQOM7R7gkCQLgV8D7p2ZHk/NAOPbu6rurOZKjnuAPWaoyz3rZWzjlPkXervdzKwaYGwT/n840zoVACOSHAUsAO4ERnZ4PwUWVtXmUZesjng78Fma7y68NsmLZ6yzU9TH2Ea8jeYT5Xarj7H9PXAk8FbgSuDhmeprP/oY39fT3GH3VcABwLdmrLNTtLWxjS1TVdfRfDoet9z2Zqpjm+T/wxnVuQBoP82fC7ye3m5pAXAY8In2+w4X03w62e70OTaS7AXsW1W3b/NO9qnPsZ0OvK6q3t2WX7qt+9mvPsf3n4Bbaaa6/lttp9d09zK2MWWYqNz2ps+xbTe2yzd1W2lPwHwWeFdVrae3W1oAfJ/m/kUAw8D6bdjNvgwwNoCXAJdt0w4OYICxPRXYL8njaG5GuL3uIPsaX1U9wq9ulvi327ibfellbOOUYbxyM9bpHg0wtu1HVXXmAbwJ2ARc3T7+gOYbzB8FvgvsOars1aOWn0izg/w6zTeZd5/tsUzX2Nr1/0VzR9dZH8c0/7u9EPgBcD/wGZoTdLM+nmn+t1sJHDPbYxhkbOOUWUZzPmPc92B7efQ7ton+LWfj0flvArdn5JcCq6uZ4tlpOLYd1848vl7HtiO+BztanzsfAJLUVZ06ByBJ+hUDQJI6ygCQpI4yACSpowwASeqo/wefcQNXCpPrDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f8eec0518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 债务压力：准货币资金与有息负债的差额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>货币资金(元)</th>\n",
       "      <td>3,448,409,300</td>\n",
       "      <td>2,581,883,300</td>\n",
       "      <td>2,196,706,800</td>\n",
       "      <td>4,054,121,700</td>\n",
       "      <td>3,921,052,700</td>\n",
       "      <td>3,802,201,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>交易性金融资产(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1,360,000,000</td>\n",
       "      <td>2,352,000,000</td>\n",
       "      <td>2,872,312,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他流动资产里的理财产品</th>\n",
       "      <td>0</td>\n",
       "      <td>1,500,000,000</td>\n",
       "      <td>2,570,000,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他流动资产里的结构性存款</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>准货币资金</th>\n",
       "      <td>3,448,409,300</td>\n",
       "      <td>4,081,883,300</td>\n",
       "      <td>4,766,706,800</td>\n",
       "      <td>5,414,121,700</td>\n",
       "      <td>6,273,052,700</td>\n",
       "      <td>6,674,513,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>短期借款(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6,076,200</td>\n",
       "      <td>29,616,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一年内到期的非流动负债(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5,387,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长期借款(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应付债券(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长期应付款</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>有息负债总额</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6,076,200</td>\n",
       "      <td>35,004,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总货币资金与有息负债之差</th>\n",
       "      <td>3,448,409,300</td>\n",
       "      <td>4,081,883,300</td>\n",
       "      <td>4,766,706,800</td>\n",
       "      <td>5,414,121,700</td>\n",
       "      <td>6,266,976,500</td>\n",
       "      <td>6,639,509,500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2016           2017           2018           2019  \\\n",
       "货币资金(元)         3,448,409,300  2,581,883,300  2,196,706,800  4,054,121,700   \n",
       "交易性金融资产(元)                  0              0              0  1,360,000,000   \n",
       "其他流动资产里的理财产品                0  1,500,000,000  2,570,000,000              0   \n",
       "其他流动资产里的结构性存款               0              0              0              0   \n",
       "准货币资金           3,448,409,300  4,081,883,300  4,766,706,800  5,414,121,700   \n",
       "短期借款(元)                     0              0              0              0   \n",
       "一年内到期的非流动负债(元)              0              0              0              0   \n",
       "长期借款(元)                     0              0              0              0   \n",
       "应付债券(元)                     0              0              0              0   \n",
       "长期应付款                       0              0              0              0   \n",
       "有息负债总额                      0              0              0              0   \n",
       "总货币资金与有息负债之差    3,448,409,300  4,081,883,300  4,766,706,800  5,414,121,700   \n",
       "\n",
       "                         2020           2021  \n",
       "货币资金(元)         3,921,052,700  3,802,201,300  \n",
       "交易性金融资产(元)      2,352,000,000  2,872,312,500  \n",
       "其他流动资产里的理财产品                0              0  \n",
       "其他流动资产里的结构性存款               0              0  \n",
       "准货币资金           6,273,052,700  6,674,513,800  \n",
       "短期借款(元)             6,076,200     29,616,700  \n",
       "一年内到期的非流动负债(元)              0      5,387,600  \n",
       "长期借款(元)                     0              0  \n",
       "应付债券(元)                     0              0  \n",
       "长期应付款                       0              0  \n",
       "有息负债总额              6,076,200     35,004,300  \n",
       "总货币资金与有息负债之差    6,266,976,500  6,639,509,500  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = ana.init_table('t3')\n",
    "t3['准货币资金'] = t3.T[:4].sum()\n",
    "t3['有息负债总额'] = t3.T[5:10].sum()\n",
    "t3['总货币资金与有息负债之差'] = t3['准货币资金'] - t3['有息负债总额']\n",
    "\n",
    "ana.format_show_table('t3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f8ff05f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f8ff1d2b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t3', image_index=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 公司竞争力：应付预收减应收预付的差额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>其中：应付票据(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>365,613,300</td>\n",
       "      <td>411,415,000</td>\n",
       "      <td>603,308,600</td>\n",
       "      <td>751,802,500</td>\n",
       "      <td>962,665,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应付账款(元)</th>\n",
       "      <td>910,854,400</td>\n",
       "      <td>1,045,259,500</td>\n",
       "      <td>1,195,563,100</td>\n",
       "      <td>1,395,061,300</td>\n",
       "      <td>1,723,832,200</td>\n",
       "      <td>2,181,900,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>预收款项(元)</th>\n",
       "      <td>840,328,500</td>\n",
       "      <td>735,005,100</td>\n",
       "      <td>1,170,088,500</td>\n",
       "      <td>1,092,261,300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合同负债(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>949,591,228</td>\n",
       "      <td>1,026,782,402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应付与预收合计</th>\n",
       "      <td>1,751,182,900</td>\n",
       "      <td>2,145,877,900</td>\n",
       "      <td>2,777,066,600</td>\n",
       "      <td>3,090,631,200</td>\n",
       "      <td>3,425,225,928</td>\n",
       "      <td>4,171,348,202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其中：应收票据(元)</th>\n",
       "      <td>637,529,200</td>\n",
       "      <td>1,007,950,700</td>\n",
       "      <td>1,268,146,300</td>\n",
       "      <td>986,693,100</td>\n",
       "      <td>1,832,701,400</td>\n",
       "      <td>1,330,193,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合同资产(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收款项融资</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>408,972,104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收账款(元)</th>\n",
       "      <td>331,595,200</td>\n",
       "      <td>371,167,700</td>\n",
       "      <td>446,773,100</td>\n",
       "      <td>725,630,900</td>\n",
       "      <td>1,008,235,900</td>\n",
       "      <td>1,597,692,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>预付款项(元)</th>\n",
       "      <td>32,828,400</td>\n",
       "      <td>58,386,100</td>\n",
       "      <td>59,485,900</td>\n",
       "      <td>50,113,500</td>\n",
       "      <td>69,889,400</td>\n",
       "      <td>131,162,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收与预付合计</th>\n",
       "      <td>1,001,952,800</td>\n",
       "      <td>1,437,504,500</td>\n",
       "      <td>1,774,405,300</td>\n",
       "      <td>2,171,409,604</td>\n",
       "      <td>2,910,826,700</td>\n",
       "      <td>3,059,048,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应付预收减应收预付的差额</th>\n",
       "      <td>749,230,100</td>\n",
       "      <td>708,373,400</td>\n",
       "      <td>1,002,661,300</td>\n",
       "      <td>919,221,596</td>\n",
       "      <td>514,399,228</td>\n",
       "      <td>1,112,299,402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       2016           2017           2018           2019  \\\n",
       "其中：应付票据(元)                0    365,613,300    411,415,000    603,308,600   \n",
       "应付账款(元)         910,854,400  1,045,259,500  1,195,563,100  1,395,061,300   \n",
       "预收款项(元)         840,328,500    735,005,100  1,170,088,500  1,092,261,300   \n",
       "合同负债(元)                   0              0              0              0   \n",
       "应付与预收合计       1,751,182,900  2,145,877,900  2,777,066,600  3,090,631,200   \n",
       "其中：应收票据(元)      637,529,200  1,007,950,700  1,268,146,300    986,693,100   \n",
       "合同资产(元)                   0              0              0              0   \n",
       "应收款项融资                    0              0              0    408,972,104   \n",
       "应收账款(元)         331,595,200    371,167,700    446,773,100    725,630,900   \n",
       "预付款项(元)          32,828,400     58,386,100     59,485,900     50,113,500   \n",
       "应收与预付合计       1,001,952,800  1,437,504,500  1,774,405,300  2,171,409,604   \n",
       "应付预收减应收预付的差额    749,230,100    708,373,400  1,002,661,300    919,221,596   \n",
       "\n",
       "                       2020           2021  \n",
       "其中：应付票据(元)      751,802,500    962,665,500  \n",
       "应付账款(元)       1,723,832,200  2,181,900,300  \n",
       "预收款项(元)                   0              0  \n",
       "合同负债(元)         949,591,228  1,026,782,402  \n",
       "应付与预收合计       3,425,225,928  4,171,348,202  \n",
       "其中：应收票据(元)    1,832,701,400  1,330,193,900  \n",
       "合同资产(元)                   0              0  \n",
       "应收款项融资                    0              0  \n",
       "应收账款(元)       1,008,235,900  1,597,692,900  \n",
       "预付款项(元)          69,889,400    131,162,000  \n",
       "应收与预付合计       2,910,826,700  3,059,048,800  \n",
       "应付预收减应收预付的差额    514,399,228  1,112,299,402  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = ana.init_table('t4')\n",
    "t4['应付与预收合计'] = t4.T[:4].sum()\n",
    "t4['应收与预付合计'] = t4.T[5:10].sum()\n",
    "t4['应付预收减应收预付的差额'] = t4['应付与预收合计'] - t4['应收与预付合计']\n",
    "\n",
    "ana.format_show_table('t4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f900210f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f900ea518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t4', image_index=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 产品竞争力：应收账款+合同资产占总资产的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>合同资产(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收账款(元)</th>\n",
       "      <td>331,595,200</td>\n",
       "      <td>371,167,700</td>\n",
       "      <td>446,773,100</td>\n",
       "      <td>725,630,900</td>\n",
       "      <td>1,008,235,900</td>\n",
       "      <td>1,597,692,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收账款加合同资产占总资产的比率</th>\n",
       "      <td>5.17%</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>4.73%</td>\n",
       "      <td>6.81%</td>\n",
       "      <td>8.09%</td>\n",
       "      <td>11.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>应收账款+合同资产</th>\n",
       "      <td>331,595,200</td>\n",
       "      <td>371,167,700</td>\n",
       "      <td>446,773,100</td>\n",
       "      <td>725,630,900</td>\n",
       "      <td>1,008,235,900</td>\n",
       "      <td>1,597,692,900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           2016           2017           2018            2019  \\\n",
       "合同资产(元)                       0              0              0               0   \n",
       "应收账款(元)             331,595,200    371,167,700    446,773,100     725,630,900   \n",
       "资产合计(元)           6,415,202,500  7,926,615,200  9,455,361,500  10,651,922,600   \n",
       "应收账款加合同资产占总资产的比率          5.17%          4.68%          4.73%           6.81%   \n",
       "应收账款+合同资产           331,595,200    371,167,700    446,773,100     725,630,900   \n",
       "\n",
       "                            2020            2021  \n",
       "合同资产(元)                        0               0  \n",
       "应收账款(元)            1,008,235,900   1,597,692,900  \n",
       "资产合计(元)           12,457,568,300  13,906,035,200  \n",
       "应收账款加合同资产占总资产的比率           8.09%          11.49%  \n",
       "应收账款+合同资产          1,008,235,900   1,597,692,900  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5 = ana.init_table('t5')\n",
    "t5['应收账款+合同资产'] = t5.T[:2].sum()\n",
    "t5['应收账款加合同资产占总资产的比率'] = t5['应收账款+合同资产'] / t5['资产合计(元)']\n",
    "\n",
    "ana.format_show_table('t5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f90272ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主业专注度：投资类资产占总资产的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>以公允价值计量且其变动计入当期损益的金融资产(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>债权投资(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>可供出售金融资产(元)</th>\n",
       "      <td>27,734,000</td>\n",
       "      <td>147,734,000</td>\n",
       "      <td>119,948,500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他权益工具投资(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102,116,000</td>\n",
       "      <td>102,116,000</td>\n",
       "      <td>2,116,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>持有至到期投资(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他非流动金融资产(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长期股权投资(元)</th>\n",
       "      <td>817,900</td>\n",
       "      <td>3,815,200</td>\n",
       "      <td>2,617,900</td>\n",
       "      <td>4,168,300</td>\n",
       "      <td>3,452,800</td>\n",
       "      <td>5,405,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投资性房地产(元)</th>\n",
       "      <td>139,500</td>\n",
       "      <td>130,600</td>\n",
       "      <td>121,600</td>\n",
       "      <td>112,600</td>\n",
       "      <td>2,591,000</td>\n",
       "      <td>11,085,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投资类资产合计</th>\n",
       "      <td>28,691,400</td>\n",
       "      <td>151,679,800</td>\n",
       "      <td>122,688,000</td>\n",
       "      <td>106,396,900</td>\n",
       "      <td>108,159,800</td>\n",
       "      <td>18,607,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投资类资产占总资产的比率</th>\n",
       "      <td>0.45%</td>\n",
       "      <td>1.91%</td>\n",
       "      <td>1.30%</td>\n",
       "      <td>1.00%</td>\n",
       "      <td>0.87%</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    2016           2017           2018  \\\n",
       "以公允价值计量且其变动计入当期损益的金融资产(元)              0              0              0   \n",
       "债权投资(元)                                0              0              0   \n",
       "可供出售金融资产(元)                   27,734,000    147,734,000    119,948,500   \n",
       "其他权益工具投资(元)                            0              0              0   \n",
       "持有至到期投资(元)                             0              0              0   \n",
       "其他非流动金融资产(元)                           0              0              0   \n",
       "长期股权投资(元)                        817,900      3,815,200      2,617,900   \n",
       "投资性房地产(元)                        139,500        130,600        121,600   \n",
       "投资类资产合计                       28,691,400    151,679,800    122,688,000   \n",
       "资产合计(元)                    6,415,202,500  7,926,615,200  9,455,361,500   \n",
       "投资类资产占总资产的比率                       0.45%          1.91%          1.30%   \n",
       "\n",
       "                                     2019            2020            2021  \n",
       "以公允价值计量且其变动计入当期损益的金融资产(元)               0               0               0  \n",
       "债权投资(元)                                 0               0               0  \n",
       "可供出售金融资产(元)                             0               0               0  \n",
       "其他权益工具投资(元)                   102,116,000     102,116,000       2,116,000  \n",
       "持有至到期投资(元)                              0               0               0  \n",
       "其他非流动金融资产(元)                            0               0               0  \n",
       "长期股权投资(元)                       4,168,300       3,452,800       5,405,100  \n",
       "投资性房地产(元)                         112,600       2,591,000      11,085,900  \n",
       "投资类资产合计                       106,396,900     108,159,800      18,607,000  \n",
       "资产合计(元)                    10,651,922,600  12,457,568,300  13,906,035,200  \n",
       "投资类资产占总资产的比率                        1.00%           0.87%           0.13%  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6 = ana.init_table('t6')\n",
    "t6['投资类资产合计'] = t6.T[:8].sum()\n",
    "t6['投资类资产占总资产的比率'] = t6['投资类资产合计'] / t6['资产合计(元)']\n",
    "\n",
    "ana.format_show_table('t6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f9016d470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t6')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 存货暴雷风险：存货和应收账款占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>存货(元)</th>\n",
       "      <td>914,493,000</td>\n",
       "      <td>1,112,902,200</td>\n",
       "      <td>1,347,112,700</td>\n",
       "      <td>1,339,176,900</td>\n",
       "      <td>1,386,089,300</td>\n",
       "      <td>1,772,231,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>存货占总资产的比率</th>\n",
       "      <td>14.26%</td>\n",
       "      <td>14.04%</td>\n",
       "      <td>14.25%</td>\n",
       "      <td>12.57%</td>\n",
       "      <td>11.13%</td>\n",
       "      <td>12.74%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2016           2017           2018            2019  \\\n",
       "存货(元)        914,493,000  1,112,902,200  1,347,112,700   1,339,176,900   \n",
       "资产合计(元)    6,415,202,500  7,926,615,200  9,455,361,500  10,651,922,600   \n",
       "存货占总资产的比率         14.26%         14.04%         14.25%          12.57%   \n",
       "\n",
       "                     2020            2021  \n",
       "存货(元)       1,386,089,300   1,772,231,600  \n",
       "资产合计(元)    12,457,568,300  13,906,035,200  \n",
       "存货占总资产的比率          11.13%          12.74%  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7 = ana.init_table('t7')\n",
    "t7['存货占总资产的比率'] = t7['存货(元)'] / t7[ '资产合计(元)']\n",
    "\n",
    "ana.format_show_table('t7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f901d9160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from analysis.utils import plot_show\n",
    "\n",
    "tmp_df = pd.merge(\n",
    "    t7['存货占总资产的比率'], \n",
    "    t5['应收账款加合同资产占总资产的比率'],\n",
    "    right_index=True,\n",
    "    left_index=True\n",
    ")\n",
    "plot_show(tmp_df, image_title=images['t7'][0], y_label='', y_format='', save_image=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 商誉暴雷风险：商誉占总资产的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>商誉(元)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80,589,600</td>\n",
       "      <td>80,589,600</td>\n",
       "      <td>80,589,600</td>\n",
       "      <td>80,589,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产合计(元)</th>\n",
       "      <td>6,415,202,500</td>\n",
       "      <td>7,926,615,200</td>\n",
       "      <td>9,455,361,500</td>\n",
       "      <td>10,651,922,600</td>\n",
       "      <td>12,457,568,300</td>\n",
       "      <td>13,906,035,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>商誉占总资产的比率</th>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.85%</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>0.65%</td>\n",
       "      <td>0.58%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2016           2017           2018            2019  \\\n",
       "商誉(元)                  0              0     80,589,600      80,589,600   \n",
       "资产合计(元)    6,415,202,500  7,926,615,200  9,455,361,500  10,651,922,600   \n",
       "商誉占总资产的比率          0.00%          0.00%          0.85%           0.76%   \n",
       "\n",
       "                     2020            2021  \n",
       "商誉(元)          80,589,600      80,589,600  \n",
       "资产合计(元)    12,457,568,300  13,906,035,200  \n",
       "商誉占总资产的比率           0.65%           0.58%  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t8 = ana.init_table('t8')\n",
    "t8['商誉占总资产的比率'] = t8['商誉(元)'] / t8[ '资产合计(元)']\n",
    "\n",
    "ana.format_show_table('t8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f90231438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana.show_plot('t8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出分析报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文档 [DEBT-002508-分析企业牛不牛（2016~2021）.docx] 已输出到 [dist] 目录下。\n"
     ]
    }
   ],
   "source": [
    "# ReportDocument(ana).save()\n",
    "from analysis.utils import read_company_code\n",
    "\n",
    "start = ana.tables['t1'].index[0]\n",
    "end = ana.tables['t1'].index[-1]\n",
    "\n",
    "name = f\"DEBT-{read_company_code()}-分析企业牛不牛（{start}~{end}）.docx\"\n",
    "doc = ReportDocument(ana, doc_name=name)\n",
    "doc.save()\n",
    "\n",
    "print(f\"文档 [{name}] 已输出到 [dist] 目录下。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
